Predicting EA Sports FIFA Team of the Season in Europe’s Five Leagues
Introduction
The video game FIFA, which is developed by Electronic Arts (EA) Sports, has become the most popular sports video game in the world in recent years, largely due to its game mode Ultimate Team. The objective of Ultimate Team is to build the best team possible through both buying and selling players, as well as buying packs of cards similarly to how people buy soccer trading cards in real life. Each player receives ratings in various categories based on their real life abilities, and each of these ratings factor into their overall rating. At the end of each season, EA Sports creates a Team of the Season (TOTS), where they select the best player at each position in each league from that season based on how they performed in real life. The players who receive TOTS cards also receive a boost to their overall rating to reflect their abilities in real life. Although most of their choices for TOTS are understandable, there are some choices that confuse and sometimes anger fans. Along with this, EA has never explained how they make their choices. Through the use of machine learning methods and predictive modeling, we aim to determine which variables are most important when choosing a player for TOTS, as well as predict the Team of the Season for Europe’s top five leagues based on this season’s statistics.
Methods and Materials
Materials: We retrieved complete player datasets for FIFA 17, FIFA 18, and FIFA 19 from here. We retrieved real life statistics from the 2016-2017, 2017-2018, and 2018-2019 seasons from fbref.com. We did not use data from the 2019-2020 season because COVID-19 caused each season to prematurely end in March of 2020.
Methods:
English Premier League
prem_modeling %>%
select(where(is.numeric)) %>%
pivot_longer(cols = everything(),
names_to = "variable",
values_to = "value") %>%
ggplot(aes(x = value)) +
geom_histogram(bins = 30) +
facet_wrap(vars(variable),
scales = "free")## Warning: Removed 19222 rows containing non-finite values (stat_bin).
theme_set(theme_cowplot())
prem_prop <- prem_modeling %>%
rename(Revision = revision) %>%
ggplot(aes(x = Revision, fill = Revision)) +
geom_bar() +
scale_fill_manual(values = c("TOTS" = "blue", "Normal" = "gold")) +
# CHANGES
labs(x = "Revision", y = "Count", title = "Proportion of Revisions Within Premier League Dataset Selected for TOTS") +
theme(plot.title = element_text(hjust = .2, size = 13.5)) +
geom_vline(xintercept = 0, linetype = "dotted")
prem_prop_logo <- ggdraw() + draw_image("https://www.fifplay.com/img/public/premier-league-2-logo.png", x = .42, y = .23, scale = .25) +
draw_plot(prem_prop)
prem_prop_logo
prem_train_metrics <- prem_training %>%
mutate(Type = "Training") %>%
rename(Revision = revision) %>%
group_by(Revision, Type) %>%
summarize(Goals = mean(Gls, na.rm = T), Assists = mean(Ast, na.rm = T), `Non PK Goals` = mean(Non_PK_G, na.rm = T), PK = mean(PK, na.rm = T), `Team Rank` = mean(Rk, na.rm = T), `Minutes Per 90` = mean(Min, na.rm = T)/90 , `Goals SD` = sd(Gls, na.rm = T), `Assists SD` = sd(Ast, na.rm = T), `Non PK Goals SD` = sd(Non_PK_G, na.rm = T),`Team Rank SD` = sd(Rk, na.rm = T), `Minutes Per 90 SD` = sd(Min, na.rm = T)/90)
prem_test_metrics <- prem_testing %>%
mutate(Type = "Testing") %>%
rename(Revision = revision) %>%
group_by(Revision, Type) %>%
summarize(Goals = mean(Gls, na.rm = T), Assists = mean(Ast, na.rm = T), `Non PK Goals` = mean(Non_PK_G, na.rm = T), PK = mean(PK, na.rm = T), `Team Rank` = mean(Rk, na.rm = T), `Minutes Per 90` = mean(Min, na.rm = T)/90 , `Goals SD` = sd(Gls, na.rm = T), `Assists SD` = sd(Ast, na.rm = T), `Non PK Goals SD` = sd(Non_PK_G, na.rm = T),`Team Rank SD` = sd(Rk, na.rm = T), `Minutes Per 90 SD` = sd(Min, na.rm = T)/90)
prem_rebound_split <- rbind(prem_train_metrics, prem_test_metrics) %>% arrange(Revision)
prem_metrics_table <- formattable(prem_rebound_split[1:4,1:13])
kable(prem_metrics_table, align = c(rep('c', 1))) %>%
row_spec(0) %>%
kable_styling(full_width = F) %>%
add_header_above(c("Premier League Training and Testing Group Comparison for Suspected KPIs" = 13), background = "purple", color = "white")| Revision | Type | Goals | Assists | Non PK Goals | PK | Team Rank | Minutes Per 90 | Goals SD | Assists SD | Non PK Goals SD | Team Rank SD | Minutes Per 90 SD |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Normal | Training | 3.055851 | 2.327128 | 2.781915 | 0.2739362 | 11.069149 | 26.94010 | 3.649960 | 2.360092 | 3.264401 | 5.455811 | 5.377800 |
| Normal | Testing | 3.200000 | 2.128000 | 2.872000 | 0.3280000 | 11.816000 | 27.45058 | 3.632292 | 2.094447 | 3.113384 | 5.492493 | 5.310808 |
| TOTS | Training | 8.942308 | 5.250000 | 8.269231 | 0.6730769 | 3.557692 | 31.76645 | 8.756876 | 4.167451 | 7.819321 | 3.268468 | 3.829581 |
| TOTS | Testing | 10.470588 | 7.352941 | 10.117647 | 0.3529412 | 4.058823 | 29.53987 | 8.768678 | 4.372373 | 8.388104 | 3.230143 | 4.999096 |
## Truth
## Prediction Normal TOTS
## Normal 116 8
## TOTS 9 9prem_ranger_test <- prem_testing %>%
bind_cols(predict(prem_ranger_fit, new_data = prem_testing, type = "prob")) %>%
bind_cols(predict(prem_ranger_fit, new_data = prem_testing))
prem_rf_preds <- prem_ranger_test %>%
conf_mat(revision, .pred_class)
prem_rf_ggconfusion <- autoplot(prem_rf_preds, type = "heatmap") + labs(title = "Confusion Matrix of Premier League Random Forest Model") + scale_fill_gradient(low = "blue", high = "red") + theme(plot.title = element_text(hjust = .5, size = 15))
prem_rf_ggconfusion
## Truth
## Prediction Normal TOTS
## Normal 117 8
## TOTS 8 9## Player revision position Int TklW OG PKcon Nation
## 1 Eric Dier 17 Normal CDM 37 34 0 0 ENG
## 2 Adam Lallana 17 TOTS CM 20 35 0 0 ENG
## 3 Antonio Valencia 17 TOTS RB 43 44 0 0 ECU
## 4 Victor Wanyama 17 Normal CDM 39 64 0 0 KEN
## 5 Philippe Coutinho 17 Normal LW 18 25 0 0 BRA
## 6 Sergio Aguero 18 TOTS ST 8 5 0 0 ARG
## 7 Ben Davies 18 TOTS LB 26 26 0 0 WAL
## 8 Eric Dier 18 Normal CB 30 35 0 0 ENG
## 9 Abdoulaye Doucoure 18 TOTS CDM 41 41 0 1 FRA
## 10 Andrew Robertson 18 TOTS LB 24 21 0 0 SCO
## 11 Antonio Valencia 18 Normal RB 43 37 0 0 ECU
## 12 Ben Chilwell 19 Normal LB 34 36 0 0 ENG
## 13 Christian Eriksen 19 TOTS CAM 11 27 0 0 DEN
## 14 James Maddison 19 TOTS CAM 12 34 0 0 ENG
## 15 James McArthur 19 Normal CM 37 56 0 1 SCO
## 16 Callum Wilson 19 Normal ST 1 9 0 0 ENG
## Squad Age Born MP Min minutes_played_divided_by90 Gls Ast
## 1 Tottenham 22 1994 36 3043 33.8 2 1
## 2 Liverpool 28 1988 31 2348 26.1 8 6
## 3 Manchester Utd 30 1985 28 2483 27.6 1 3
## 4 Tottenham 25 1991 36 3012 33.5 4 1
## 5 Liverpool 24 1992 31 2227 24.7 13 8
## 6 Manchester City 29 1988 25 1963 21.8 21 6
## 7 Tottenham 24 1993 29 2324 25.8 2 6
## 8 Tottenham 23 1994 34 2824 31.4 0 2
## 9 Watford 24 1993 37 3324 36.9 7 3
## 10 Liverpool 23 1994 22 1940 21.6 1 5
## 11 Manchester Utd 31 1985 31 2740 30.4 3 1
## 12 Leicester City 21 1996 36 3240 36.0 0 4
## 13 Tottenham 26 1992 35 2774 30.8 8 12
## 14 Leicester City 21 1996 36 2831 31.5 7 7
## 15 Crystal Palace 30 1987 38 3058 34.0 3 6
## 16 Bournemouth 26 1992 30 2528 28.1 14 9
## Non_PK_G PK PKatt CrdY CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90
## 1 2 0 0 6 0 0.06 0.03 0.09 0.06
## 2 8 0 0 3 0 0.31 0.23 0.54 0.31
## 3 1 0 0 5 0 0.04 0.11 0.14 0.04
## 4 4 0 0 10 0 0.12 0.03 0.15 0.12
## 5 13 0 0 2 0 0.53 0.32 0.85 0.53
## 6 17 4 4 2 0 0.96 0.28 1.24 0.78
## 7 2 0 0 3 0 0.08 0.23 0.31 0.08
## 8 0 0 0 4 0 0.00 0.06 0.06 0.00
## 9 7 0 0 10 0 0.19 0.08 0.27 0.19
## 10 1 0 0 2 0 0.05 0.23 0.28 0.05
## 11 3 0 0 7 0 0.10 0.03 0.13 0.10
## 12 0 0 0 4 0 0.00 0.11 0.11 0.00
## 13 8 0 0 3 0 0.26 0.39 0.65 0.26
## 14 6 1 2 4 1 0.22 0.22 0.45 0.19
## 15 3 0 0 7 0 0.09 0.18 0.26 0.09
## 16 13 1 2 3 0 0.50 0.32 0.82 0.46
## G_plus_A_minus_PK_per90 Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS
## 1 0.09 2 86 26 60 86 31639 0.010 0.990
## 2 0.54 4 78 42 36 76 53016 0.990 0.010
## 3 0.14 6 54 29 25 69 75290 0.590 0.410
## 4 0.15 2 86 26 60 86 31639 0.000 1.000
## 5 0.85 4 78 42 36 76 53016 0.090 0.910
## 6 1.05 1 106 27 79 100 54070 0.830 0.170
## 7 0.31 3 74 36 38 77 67953 0.500 0.500
## 8 0.06 3 74 36 38 77 67953 0.450 0.550
## 9 0.27 14 44 64 -20 41 20231 0.945 0.055
## 10 0.28 4 84 38 46 75 53049 0.990 0.010
## 11 0.13 2 68 28 40 81 74976 0.200 0.800
## 12 0.11 9 51 48 3 52 31851 0.475 0.525
## 13 0.65 4 67 39 28 71 54216 0.840 0.160
## 14 0.41 9 51 48 3 52 31851 0.970 0.030
## 15 0.26 12 51 53 -2 49 25455 0.485 0.515
## 16 0.78 14 56 70 -14 45 10532 0.190 0.810
## .pred_class
## 1 TOTS
## 2 Normal
## 3 Normal
## 4 TOTS
## 5 TOTS
## 6 Normal
## 7 Normal
## 8 TOTS
## 9 Normal
## 10 Normal
## 11 TOTS
## 12 TOTS
## 13 Normal
## 14 Normal
## 15 TOTS
## 16 TOTS## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 Harry Kane Normal ST 13 10 0 0 ENG Tottenham 27
## 2 Mohamed Salah Normal RW 6 13 0 0 EGY Liverpool 28
## 3 Ollie Watkins Normal ST 9 20 0 0 ENG Aston Villa 25
## 4 Jamie Vardy Normal ST 7 8 0 0 ENG Leicester City 34
## 5 Timo Werner Normal ST 6 15 0 0 GER Chelsea 25
## Born MP Starts Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt
## 1 1993 30 30 2632 29.2 21 13 17 4 4
## 2 1992 32 29 2633 29.3 20 3 14 6 6
## 3 1995 32 32 2880 32.0 12 4 11 1 2
## 4 1987 29 26 2401 26.7 13 8 7 6 7
## 5 1996 31 25 2243 24.9 6 6 6 0 0
## CrdY CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90
## 1 1 0 0.72 0.44 1.16 0.58
## 2 0 0 0.68 0.10 0.79 0.48
## 3 2 0 0.37 0.12 0.50 0.34
## 4 1 0 0.49 0.30 0.79 0.26
## 5 1 0 0.24 0.24 0.48 0.24
## G_plus_A_minus_PK_per90 Matches Rk GF GA GD Pts Attendance .pred_Normal
## 1 1.03 Matches 7 56 38 18 53 125 0.070
## 2 0.58 Matches 6 55 39 16 54 353 0.470
## 3 0.47 Matches 11 46 37 9 45 NA 0.570
## 4 0.56 Matches 3 60 38 22 62 NA 0.575
## 5 0.48 Matches 4 51 31 20 58 125 0.590
## .pred_TOTS .pred_class
## 1 0.930 TOTS
## 2 0.530 TOTS
## 3 0.430 Normal
## 4 0.425 Normal
## 5 0.410 Normal## Player revision position Int TklW OG PKcon Nation Squad
## 1 Son Heung min Normal LM 20 12 0 0 KOR Tottenham
## 2 Tomas Soucek Normal CDM 45 43 1 0 CZE West Ham
## 3 Bruno Fernandes Normal CAM 18 36 0 1 POR Manchester Utd
## 4 Pierre Hojbjerg Normal CDM 38 71 0 1 DEN Tottenham
## 5 Rodri Normal CDM 31 54 0 0 ESP Manchester City
## Age Born MP Starts Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt
## 1 28 1992 32 31 2665 29.6 15 9 14 1 1
## 2 26 1995 33 33 2969 33.0 9 1 9 0 0
## 3 26 1994 33 32 2821 31.3 16 11 8 8 9
## 4 25 1995 33 33 2970 33.0 1 3 1 0 0
## 5 24 1996 29 27 2353 26.1 2 1 1 1 1
## CrdY CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90
## 1 0 0 0.51 0.30 0.81 0.47
## 2 6 1 0.27 0.03 0.30 0.27
## 3 5 0 0.51 0.35 0.86 0.26
## 4 7 0 0.03 0.09 0.12 0.03
## 5 4 0 0.08 0.04 0.11 0.04
## G_plus_A_minus_PK_per90 Matches Rk GF GA GD Pts Attendance .pred_Normal
## 1 0.78 Matches 7 56 38 18 53 125 0.130
## 2 0.30 Matches 5 53 43 10 55 118 0.310
## 3 0.61 Matches 2 64 35 29 67 NA 0.375
## 4 0.12 Matches 7 56 38 18 53 125 0.390
## 5 0.08 Matches 1 69 24 45 77 NA 0.400
## .pred_TOTS .pred_class
## 1 0.870 TOTS
## 2 0.690 TOTS
## 3 0.625 TOTS
## 4 0.610 TOTS
## 5 0.600 TOTS## Player revision position Int TklW OG PKcon Nation Squad
## 1 Harry Maguire Normal CB 60 18 0 0 ENG Manchester Utd
## 2 Ruben Dias Normal CB 21 19 1 1 POR Manchester City
## 3 Aaron Wan Bissaka Normal RB 63 48 0 0 ENG Manchester Utd
## 4 Edouard Mendy Normal LB 0 0 0 1 SEN Chelsea
## 5 Matt Targett Normal LB 37 46 0 1 ENG Aston Villa
## Age Born MP Starts Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt
## 1 28 1993 33 33 2970 33.0 2 1 2 0 0
## 2 23 1997 29 29 2573 28.6 1 0 1 0 0
## 3 23 1997 31 31 2790 31.0 2 2 2 0 0
## 4 29 1992 27 27 2430 27.0 0 0 0 0 0
## 5 25 1995 32 32 2864 31.8 0 1 0 0 0
## CrdY CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90
## 1 10 0 0.06 0.03 0.09 0.06
## 2 3 0 0.03 0.00 0.03 0.03
## 3 3 0 0.06 0.06 0.13 0.06
## 4 1 0 0.00 0.00 0.00 0.00
## 5 7 0 0.00 0.03 0.03 0.00
## G_plus_A_minus_PK_per90 Matches Rk GF GA GD Pts Attendance .pred_Normal
## 1 0.09 Matches 2 64 35 29 67 NA 0.400
## 2 0.03 Matches 1 69 24 45 77 NA 0.575
## 3 0.13 Matches 2 64 35 29 67 NA 0.625
## 4 0.00 Matches 4 51 31 20 58 125 0.660
## 5 0.03 Matches 11 46 37 9 45 NA 0.665
## .pred_TOTS .pred_class
## 1 0.600 TOTS
## 2 0.425 Normal
## 3 0.375 Normal
## 4 0.340 Normal
## 5 0.335 Normal
Premier League Team of the Season
La Liga (Spain)
la_liga_train_metrics <- la_liga_training %>%
mutate(Type = "Training") %>%
rename(Revision = revision) %>%
group_by(Revision, Type) %>%
summarize(Goals = mean(Gls, na.rm = T), Assists = mean(Ast, na.rm = T), `Non PK Goals` = mean(Non_PK_G, na.rm = T), PK = mean(PK, na.rm = T), `Team Rank` = mean(Rk, na.rm = T), `Minutes Per 90` = mean(Min, na.rm = T)/90 , `Goals SD` = sd(Gls, na.rm = T), `Assists SD` = sd(Ast, na.rm = T), `Non PK Goals SD` = sd(Non_PK_G, na.rm = T),`Team Rank SD` = sd(Rk, na.rm = T), `Minutes Per 90 SD` = sd(Min, na.rm = T)/90)
la_liga_test_metrics <- la_liga_testing %>%
mutate(Type = "Testing") %>%
rename(Revision = revision) %>%
group_by(Revision, Type) %>%
summarize(Goals = mean(Gls, na.rm = T), Assists = mean(Ast, na.rm = T), `Non PK Goals` = mean(Non_PK_G, na.rm = T), PK = mean(PK, na.rm = T),`Team Rank` = mean(Rk, na.rm = T), `Minutes Per 90` = mean(Min, na.rm = T)/90, `Goals SD` = sd(Gls, na.rm = T), `Assists SD` = sd(Ast, na.rm = T), `Non PK Goals SD` = sd(Non_PK_G, na.rm = T),`Team Rank SD` = sd(Rk, na.rm = T), `Minutes Per 90 SD` = sd(Min, na.rm = T)/90)
la_liga_rebound_split <- rbind(la_liga_train_metrics, la_liga_test_metrics) %>% arrange(Revision)
la_liga_metrics_table <- formattable(la_liga_rebound_split[1:4,1:13])
kable(la_liga_metrics_table, align = c(rep('c', 1))) %>%
row_spec(0) %>%
kable_styling(full_width = F) %>%
add_header_above(c("La Liga Training and Testing Group Comparison for Suspected KPIs" = 13), background = "navy", color = "red")| Revision | Type | Goals | Assists | Non PK Goals | PK | Team Rank | Minutes Per 90 | Goals SD | Assists SD | Non PK Goals SD | Team Rank SD | Minutes Per 90 SD |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Normal | Training | 2.988764 | 2.207865 | 2.705056 | 0.2837079 | 10.772472 | 26.22697 | 3.773874 | 1.999027 | 3.360093 | 5.336711 | 4.584873 |
| Normal | Testing | 2.838983 | 2.347458 | 2.550848 | 0.2881356 | 10.686441 | 26.31591 | 3.215801 | 2.419215 | 2.784565 | 5.743371 | 4.868298 |
| TOTS | Training | 8.958333 | 5.000000 | 7.520833 | 1.4375000 | 4.083333 | 29.57315 | 8.829251 | 3.695886 | 7.795224 | 3.923922 | 3.642296 |
| TOTS | Testing | 11.933333 | 4.733333 | 10.733333 | 1.2000000 | 6.333333 | 30.12296 | 12.831138 | 3.712270 | 11.516861 | 6.488084 | 4.007976 |
## Truth
## Prediction Normal TOTS
## Normal 114 7
## TOTS 4 8## Truth
## Prediction Normal TOTS
## Normal 113 7
## TOTS 5 8la_liga_ranger_test <- la_liga_testing %>%
bind_cols(predict(la_liga_ranger_fit, new_data = la_liga_testing, type = "prob")) %>%
bind_cols(predict(la_liga_ranger_fit, new_data = la_liga_testing))
la_liga_rf_preds <- la_liga_ranger_test %>%
conf_mat(revision, .pred_class)
la_liga_rf_ggconfusion <- autoplot(la_liga_rf_preds, type = "heatmap") + labs(title = "Confusion Matrix of La Liga Random Forest Model") + scale_fill_gradient(low = "blue", high = "red") + theme(plot.title = element_text(hjust = .5, size = 15))
la_liga_rf_ggconfusion
## Player revision position Int TklW OG PKcon Nation
## 1 Kevin Prince Boateng 17 TOTS ST 19 16 0 2 GHA
## 2 Dani Carvajal 17 TOTS RB 45 41 0 0 ESP
## 3 Karim Benzema 18 Normal ST 6 6 0 0 FRA
## 4 Stefan Savic 18 Normal CB 30 10 0 0 MNE
## 5 Samuel Umtiti 18 Normal CB 30 26 0 0 FRA
## 6 Casemiro 18 TOTS CDM 41 67 0 0 BRA
## 7 Koke 18 Normal CM 23 41 0 0 ESP
## 8 Marcelo 18 Normal LB 26 32 0 0 BRA
## 9 Roberto 18 TOTS RB 0 0 0 0 ESP
## 10 Ever Banega 19 TOTS CDM 31 44 0 1 ARG
## 11 Djene 19 TOTS CB 59 36 0 3 TOG
## 12 Mario Hermoso 19 TOTS CB 25 25 0 2 ESP
## Squad Age Born MP Min minutes_played_divided_by90 Gls Ast
## 1 Las Palmas 29 1987 28 1978 22.0 10 4
## 2 Real Madrid 24 1992 23 2018 22.4 0 4
## 3 Real Madrid 29 1987 32 2149 23.9 5 9
## 4 Atlético Madrid 26 1991 27 2299 25.5 0 0
## 5 Barcelona 23 1993 25 2189 24.3 1 0
## 6 Real Madrid 25 1992 30 2589 28.8 5 3
## 7 Atlético Madrid 25 1992 35 2753 30.6 4 3
## 8 Real Madrid 29 1988 28 2262 25.1 2 6
## 9 Málaga 31 1986 34 3060 34.0 0 0
## 10 Sevilla 30 1988 32 2667 29.6 3 5
## 11 Getafe 26 1991 34 2976 33.1 0 0
## 12 Espanyol 23 1995 32 2806 31.2 3 0
## Non_PK_G PK PKatt CrdY CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90
## 1 10 0 0 11 3 0.46 0.18 0.64 0.46
## 2 0 0 0 11 0 0.00 0.18 0.18 0.00
## 3 3 2 2 0 0 0.21 0.38 0.59 0.13
## 4 0 0 0 6 0 0.00 0.00 0.00 0.00
## 5 1 0 0 7 0 0.04 0.00 0.04 0.04
## 6 5 0 0 8 0 0.17 0.10 0.28 0.17
## 7 4 0 0 3 0 0.13 0.10 0.23 0.13
## 8 2 0 0 3 1 0.08 0.24 0.32 0.08
## 9 0 0 0 0 0 0.00 0.00 0.00 0.00
## 10 1 2 2 17 2 0.10 0.17 0.27 0.03
## 11 0 0 0 13 2 0.00 0.00 0.00 0.00
## 12 3 0 0 7 0 0.10 0.00 0.10 0.10
## G_plus_A_minus_PK_per90 Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS
## 1 0.64 14 53 74 -21 39 20249 0.8351764 0.16482363
## 2 0.18 1 106 41 65 93 69426 0.5350530 0.46494701
## 3 0.50 3 94 44 50 76 66161 0.3900947 0.60990531
## 4 0.00 2 58 22 36 79 55483 0.4640527 0.53594732
## 5 0.04 1 99 29 70 93 66603 0.4775872 0.52241285
## 6 0.28 3 94 44 50 76 66161 0.5192771 0.48072292
## 7 0.23 2 58 22 36 79 55483 0.3561371 0.64386286
## 8 0.32 3 94 44 50 76 66161 0.4132756 0.58672440
## 9 0.00 20 24 61 -37 20 20420 0.8922506 0.10774940
## 10 0.20 6 62 47 15 59 35993 0.6047529 0.39524714
## 11 0.00 5 48 35 13 59 11000 0.8174812 0.18251881
## 12 0.10 7 48 50 -2 53 19388 0.9867692 0.01323077
## .pred_class
## 1 Normal
## 2 Normal
## 3 TOTS
## 4 TOTS
## 5 TOTS
## 6 Normal
## 7 TOTS
## 8 TOTS
## 9 Normal
## 10 Normal
## 11 Normal
## 12 Normal## Player revision position Int TklW OG PKcon Nation Squad
## 1 Lionel Messi Normal RW 6 12 0 0 ARG Barcelona
## 2 Karim Benzema Normal ST 11 5 0 0 FRA Real Madrid
## 3 Luis Suarez Normal ST 5 4 0 0 URU Atlético Madrid
## 4 Antoine Griezmann Normal ST 11 23 0 0 FRA Barcelona
## 5 Mikel Oyarzabal Normal LW 16 13 0 0 ESP Real Sociedad
## Age Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY
## 1 33 1987 30 2573 28.6 25 9 22 3 4 4
## 2 33 1987 29 2458 27.3 21 7 20 1 1 2
## 3 34 1987 27 2078 23.1 19 2 16 3 3 5
## 4 30 1991 30 2095 23.3 11 6 10 1 2 4
## 5 24 1997 28 2015 22.4 10 7 4 6 7 1
## CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0 0.87 0.31 1.19 0.77 1.08
## 2 0 0.77 0.26 1.03 0.73 0.99
## 3 0 0.82 0.09 0.91 0.69 0.78
## 4 0 0.47 0.26 0.73 0.43 0.69
## 5 0 0.45 0.31 0.76 0.18 0.49
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 3 76 29 47 71 NA 0.08689361 0.9131064 TOTS
## 2 Matches 2 56 24 32 71 NA 0.27670259 0.7232974 TOTS
## 3 Matches 1 60 22 38 73 NA 0.30211040 0.6978896 TOTS
## 4 Matches 3 76 29 47 71 NA 0.55263656 0.4473634 Normal
## 5 Matches 5 51 34 17 53 NA 0.61295215 0.3870478 Normal## Player revision position Int TklW OG PKcon Nation Squad
## 1 Koke Normal CM 32 46 0 0 ESP Atlético Madrid
## 2 Marcos Llorente Normal CM 27 41 0 0 ESP Atlético Madrid
## 3 Angel Correa Normal RM 19 26 0 0 ARG Atlético Madrid
## 4 Frenkie de Jong Normal CM 29 24 0 1 NED Barcelona
## 5 Saul Niguez Normal CM 21 46 0 0 ESP Atlético Madrid
## Age Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY
## 1 29 1992 32 2635 29.3 1 2 1 0 0 8
## 2 26 1995 32 2529 28.1 11 10 11 0 0 6
## 3 26 1995 33 2022 22.5 7 8 7 0 0 3
## 4 23 1997 32 2721 30.2 3 4 3 0 0 4
## 5 26 1994 28 1771 19.7 2 1 2 0 1 8
## CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0 0.03 0.07 0.10 0.03 0.10
## 2 0 0.39 0.36 0.75 0.39 0.75
## 3 0 0.31 0.36 0.67 0.31 0.67
## 4 0 0.10 0.13 0.23 0.10 0.23
## 5 0 0.10 0.05 0.15 0.10 0.15
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 1 60 22 38 73 NA 0.3416164 0.6583836 TOTS
## 2 Matches 1 60 22 38 73 NA 0.3448801 0.6551199 TOTS
## 3 Matches 1 60 22 38 73 NA 0.4786510 0.5213490 TOTS
## 4 Matches 3 76 29 47 71 NA 0.5342628 0.4657372 Normal
## 5 Matches 1 60 22 38 73 NA 0.5391104 0.4608896 Normal## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 Stefan Savic Normal CB 29 25 0 1 MNE Atlético Madrid 30
## 2 Felipe Normal CB 37 15 1 0 BRA Atlético Madrid 31
## 3 Raphael Varane Normal CB 35 11 1 0 FRA Real Madrid 28
## 4 Jordi Alba Normal LB 36 21 1 0 ESP Barcelona 32
## 5 Mario Hermoso Normal CB 28 34 1 0 ESP Atlético Madrid 25
## Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 1991 29 2593 28.8 1 0 1 0 0 13 0
## 2 1989 26 1688 18.8 0 0 0 0 0 6 0
## 3 1993 29 2580 28.7 2 0 2 0 0 2 0
## 4 1989 29 2530 28.1 3 5 3 0 0 8 0
## 5 1995 26 2181 24.2 1 1 1 0 0 5 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.03 0.00 0.03 0.03 0.03
## 2 0.00 0.00 0.00 0.00 0.00
## 3 0.07 0.00 0.07 0.07 0.07
## 4 0.11 0.18 0.28 0.11 0.28
## 5 0.04 0.04 0.08 0.04 0.08
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 1 60 22 38 73 NA 0.5102042 0.4897958 Normal
## 2 Matches 1 60 22 38 73 NA 0.5124104 0.4875896 Normal
## 3 Matches 2 56 24 32 71 NA 0.5253725 0.4746275 Normal
## 4 Matches 3 76 29 47 71 NA 0.5467540 0.4532460 Normal
## 5 Matches 1 60 22 38 73 NA 0.5682385 0.4317615 Normal
La Liga Team of the Season
French Ligue 1
## Truth
## Prediction Normal TOTS
## Normal 107 5
## TOTS 12 10## Truth
## Prediction Normal TOTS
## Normal 107 4
## TOTS 12 11## Player revision position Int TklW OG PKcon Nation Squad
## 1 Serge Aurier Normal RB 36 33 1 1 CIV Paris S-G
## 2 Blaise Matuidi Normal CDM 40 42 0 0 FRA Paris S-G
## 3 Adrien Rabiot Normal CM 38 46 0 0 FRA Paris S-G
## 4 Djibril Sidibe Normal RB 47 52 0 1 FRA Monaco
## 5 Jemerson Normal CB 54 51 0 0 BRA Monaco
## 6 Nicolas de Preville Normal ST 17 21 0 0 FRA Lille
## 7 Giovani Lo Celso Normal CAM 20 59 0 0 ARG Paris S-G
## 8 Alassane Plea Normal ST 13 9 0 0 FRA Nice
## 9 Adil Rami TOTS CB 33 20 1 0 FRA Marseille
## 10 Dani Alves Normal RB 28 52 0 0 BRA Paris S-G
## 11 Jorge Normal LB 52 33 0 0 BRA Monaco
## 12 Joao Moutinho Normal CM 39 44 0 0 POR Monaco
## 13 Houssem Aouar Normal CM 31 36 0 0 FRA Lyon
## 14 Kenny Lala TOTS RB 29 43 0 1 FRA Strasbourg
## 15 Ferland Mendy TOTS LB 25 30 0 1 FRA Lyon
## 16 Thiago Mendes TOTS CDM 51 70 0 2 BRA Lille
## Age Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY
## 1 23 1992 22 1829 20.3 0 3 0 0 0 4
## 2 29 1987 34 2415 26.8 4 4 4 0 0 4
## 3 21 1995 27 1935 21.5 3 2 3 0 0 2
## 4 24 1992 29 2321 25.8 2 5 2 0 0 7
## 5 23 1992 34 3058 34.0 2 0 2 0 0 8
## 6 25 1991 30 2059 22.9 14 5 10 4 4 2
## 7 21 1996 33 1776 19.7 4 2 4 0 0 2
## 8 24 1993 35 3041 33.8 16 4 15 1 2 7
## 9 31 1985 33 2955 32.8 1 1 1 0 0 5
## 10 34 1983 25 2065 22.9 1 4 1 0 0 7
## 11 21 1996 22 1919 21.3 1 2 1 0 0 8
## 12 30 1986 33 2802 31.1 1 4 1 0 0 6
## 13 20 1998 37 3061 34.0 7 7 7 0 0 2
## 14 26 1991 34 3060 34.0 5 9 4 1 2 4
## 15 23 1995 30 2531 28.1 2 1 2 0 0 2
## 16 26 1992 35 3074 34.2 0 5 0 0 0 10
## CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 2 0.00 0.15 0.15 0.00 0.15
## 2 0 0.15 0.15 0.30 0.15 0.30
## 3 0 0.14 0.09 0.23 0.14 0.23
## 4 0 0.08 0.19 0.27 0.08 0.27
## 5 2 0.06 0.00 0.06 0.06 0.06
## 6 0 0.61 0.22 0.83 0.44 0.66
## 7 0 0.20 0.10 0.30 0.20 0.30
## 8 0 0.47 0.12 0.59 0.44 0.56
## 9 0 0.03 0.03 0.06 0.03 0.06
## 10 1 0.04 0.17 0.22 0.04 0.22
## 11 0 0.05 0.09 0.14 0.05 0.14
## 12 0 0.03 0.13 0.16 0.03 0.16
## 13 0 0.21 0.21 0.41 0.21 0.41
## 14 0 0.15 0.26 0.41 0.12 0.38
## 15 0 0.07 0.04 0.11 0.07 0.11
## 16 0 0.00 0.15 0.15 0.00 0.15
## Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 2 83 27 56 87 45160 0.425 0.575 TOTS
## 2 2 83 27 56 87 45160 0.115 0.885 TOTS
## 3 2 83 27 56 87 45160 0.090 0.910 TOTS
## 4 1 107 31 76 95 9586 0.030 0.970 TOTS
## 5 1 107 31 76 95 9586 0.240 0.760 TOTS
## 6 11 40 47 -7 46 29487 0.495 0.505 TOTS
## 7 1 108 29 79 93 46929 0.485 0.515 TOTS
## 8 8 53 52 1 54 22876 0.360 0.640 TOTS
## 9 4 80 47 33 77 46040 0.855 0.145 Normal
## 10 1 108 29 79 93 46929 0.070 0.930 TOTS
## 11 2 85 45 40 80 9243 0.280 0.720 TOTS
## 12 2 85 45 40 80 9243 0.220 0.780 TOTS
## 13 3 70 47 23 72 49079 0.280 0.720 TOTS
## 14 11 58 48 10 49 25216 0.690 0.310 Normal
## 15 3 70 47 23 72 49079 0.770 0.230 Normal
## 16 2 68 33 35 75 34079 0.505 0.495 Normal## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 Gaetan Laborde Normal ST 14 30 0 0 FRA Montpellier 26
## 2 Kevin Volland Normal ST 3 26 0 0 GER Monaco 28
## 3 Memphis Depay Normal CF 9 5 0 0 NED Lyon 27
## 4 Kylian Mbappe Normal ST 5 4 0 0 FRA Paris S-G 22
## 5 Andy Delort Normal ST 3 15 0 0 ALG Montpellier 29
## Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 1994 34 2932 32.6 13 8 13 0 0 3 0
## 2 1992 31 2419 26.9 15 7 15 0 0 3 0
## 3 1994 34 2653 29.5 18 9 10 8 8 4 0
## 4 1998 29 2214 24.6 25 7 19 6 6 5 0
## 5 1991 26 2136 23.7 12 9 12 0 0 4 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.40 0.25 0.64 0.40 0.64
## 2 0.56 0.26 0.82 0.56 0.82
## 3 0.61 0.31 0.92 0.34 0.64
## 4 1.02 0.28 1.30 0.77 1.06
## 5 0.51 0.38 0.88 0.51 0.88
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 8 54 57 -3 47 NA 0.190 0.810 TOTS
## 2 Matches 3 71 38 33 71 NA 0.200 0.800 TOTS
## 3 Matches 4 67 35 32 67 258 0.265 0.735 TOTS
## 4 Matches 2 77 26 51 72 NA 0.320 0.680 TOTS
## 5 Matches 8 54 57 -3 47 NA 0.385 0.615 TOTS## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 Jonathan Bamba Normal LM 27 31 0 0 FRA Lille 25
## 2 Aurelien Tchouameni Normal CM 55 86 0 0 FRA Monaco 21
## 3 Mehdi Abeid Normal CDM 23 24 0 0 ALG Nantes 28
## 4 Idrissa Gana Gueye Normal CDM 22 43 0 0 SEN Paris S-G 31
## 5 Otavio Normal CDM 13 48 0 1 BRA Bordeaux 26
## Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 1996 34 2719 30.2 6 9 6 0 0 2 0
## 2 2000 32 2703 30.0 2 4 2 0 0 9 1
## 3 1992 18 1320 14.7 0 0 0 0 0 5 0
## 4 1989 25 1482 16.5 2 1 2 0 0 3 0
## 5 1994 18 1559 17.3 1 0 1 0 0 6 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.20 0.30 0.50 0.20 0.50
## 2 0.07 0.13 0.20 0.07 0.20
## 3 0.00 0.00 0.00 0.00 0.00
## 4 0.12 0.06 0.18 0.12 0.18
## 5 0.06 0.00 0.06 0.06 0.06
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 1 57 22 35 73 234 0.32 0.68 TOTS
## 2 Matches 3 71 38 33 71 NA 0.53 0.47 Normal
## 3 Matches 18 35 52 -17 31 NA 0.62 0.38 Normal
## 4 Matches 2 77 26 51 72 NA 0.62 0.38 Normal
## 5 Matches 16 36 52 -16 36 NA 0.64 0.36 Normal## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 Leonardo Balerdi Normal CB 29 17 0 0 ARG Marseille 22
## 2 Mohamed Simakan Normal CB 24 30 0 2 FRA Strasbourg 20
## 3 Flavius Daniliuc Normal CB 17 12 0 0 AUT Nice 20
## 4 Senou Coulibaly Normal CB 34 20 0 0 MLI Dijon 26
## 5 Jose Fonte Normal CB 29 24 1 1 POR Lille 37
## Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 1999 17 1363 15.1 2 0 2 0 0 5 1
## 2 2000 19 1615 17.9 1 1 1 0 0 2 0
## 3 2001 20 1341 14.9 1 0 1 0 0 0 0
## 4 1994 19 1664 18.5 2 0 2 0 0 6 1
## 5 1983 33 2915 32.4 3 0 3 0 0 6 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.13 0.00 0.13 0.13 0.13
## 2 0.06 0.06 0.11 0.06 0.11
## 3 0.07 0.00 0.07 0.07 0.07
## 4 0.11 0.00 0.11 0.11 0.11
## 5 0.09 0.00 0.09 0.09 0.09
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 6 49 42 7 55 NA 0.485 0.515 TOTS
## 2 Matches 15 43 53 -10 37 NA 0.520 0.480 Normal
## 3 Matches 9 44 45 -1 46 NA 0.530 0.470 Normal
## 4 Matches 20 23 61 -38 18 NA 0.540 0.460 Normal
## 5 Matches 1 57 22 35 73 234 0.565 0.435 Normal
Ligue 1 Team of the Season
German Bundesliga
## Truth
## Prediction Normal TOTS
## Normal 85 8
## TOTS 8 8## Truth
## Prediction Normal TOTS
## Normal 86 9
## TOTS 7 7## Player revision position Int TklW OG PKcon Nation
## 1 Kerem Demirbay 17 Normal CAM 44 33 0 0 GER
## 2 Marco Fabian 17 TOTS CAM 51 31 0 0 MEX
## 3 Vincenzo Grifo 17 TOTS LM 37 31 0 0 ITA
## 4 Javi Martinez 17 Normal CB 55 37 0 0 ESP
## 5 Michael Gregoritsch 18 TOTS CAM 12 15 0 0 AUT
## 6 Thorgan Hazard 18 TOTS LM 20 33 0 0 BEL
## 7 Naby Keita 18 TOTS CM 22 33 0 0 GUI
## 8 Andrej Kramaric 18 Normal ST 8 3 0 0 CRO
## 9 Philipp Max 18 TOTS LB 19 31 0 0 GER
## 10 Nils Petersen 18 TOTS ST 15 16 0 0 GER
## 11 Wendell 18 TOTS LB 20 25 0 0 BRA
## 12 Ishak Belfodil 19 Normal ST 2 9 0 0 ALG
## 13 Kerem Demirbay 19 TOTS CM 23 36 0 0 GER
## 14 Lukas Klostermann 19 Normal RB 24 25 0 0 GER
## 15 Andrej Kramaric 19 Normal ST 8 11 0 0 CRO
## 16 Lukasz Piszczek 19 Normal RB 31 30 0 0 POL
## Squad Age Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G
## 1 Hoffenheim 23 1993 28 2169 24.1 6 8 6
## 2 Eint Frankfurt 27 1989 24 2054 22.8 7 4 6
## 3 Freiburg 23 1993 30 2492 27.7 6 7 5
## 4 Bayern Munich 27 1988 25 2131 23.7 1 1 1
## 5 Augsburg 23 1994 32 2527 28.1 13 3 12
## 6 M'Gladbach 24 1993 34 2939 32.7 10 5 5
## 7 RB Leipzig 22 1995 27 1962 21.8 6 5 6
## 8 Hoffenheim 26 1991 34 2228 24.8 13 6 11
## 9 Augsburg 23 1993 33 2959 32.9 2 12 2
## 10 Freiburg 28 1988 32 2244 24.9 15 1 10
## 11 Leverkusen 24 1993 26 2115 23.5 2 3 0
## 12 Hoffenheim 26 1992 28 1863 20.7 16 3 16
## 13 Hoffenheim 25 1993 26 2017 22.4 4 9 4
## 14 RB Leipzig 22 1996 26 2003 22.3 5 1 5
## 15 Hoffenheim 27 1991 30 2396 26.6 17 4 12
## 16 Dortmund 33 1985 20 1756 19.5 1 6 1
## PK PKatt CrdY CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90
## 1 0 0 4 0 0.25 0.33 0.58 0.25
## 2 1 2 10 0 0.31 0.18 0.48 0.26
## 3 1 1 1 0 0.22 0.25 0.47 0.18
## 4 0 0 5 0 0.04 0.04 0.08 0.04
## 5 1 1 3 0 0.46 0.11 0.57 0.43
## 6 5 6 1 0 0.31 0.15 0.46 0.15
## 7 0 0 8 2 0.28 0.23 0.50 0.28
## 8 2 2 1 0 0.53 0.24 0.77 0.44
## 9 0 0 5 0 0.06 0.36 0.43 0.06
## 10 5 6 4 1 0.60 0.04 0.64 0.40
## 11 2 3 7 1 0.09 0.13 0.21 0.00
## 12 0 0 3 0 0.77 0.14 0.92 0.77
## 13 0 0 6 0 0.18 0.40 0.58 0.18
## 14 0 0 1 0 0.22 0.04 0.27 0.22
## 15 5 6 2 0 0.64 0.15 0.79 0.45
## 16 0 0 3 0 0.05 0.31 0.36 0.05
## G_plus_A_minus_PK_per90 Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS
## 1 0.58 4 64 37 27 62 28155 0.3821372 0.6178628
## 2 0.44 11 36 43 -7 42 49165 0.7816151 0.2183849
## 3 0.43 7 42 60 -18 48 23959 0.7579803 0.2420197
## 4 0.08 1 89 22 67 82 75000 0.3920758 0.6079242
## 5 0.53 12 43 46 -3 41 28238 0.7521182 0.2478818
## 6 0.31 9 47 52 -5 47 50986 0.6619075 0.3380925
## 7 0.50 6 57 53 4 53 39397 0.7236101 0.2763899
## 8 0.69 3 66 48 18 55 28716 0.2313905 0.7686095
## 9 0.43 12 43 46 -3 41 28238 0.6581140 0.3418860
## 10 0.44 15 32 56 -24 36 23894 0.7198035 0.2801965
## 11 0.13 5 58 44 14 55 28415 0.7132025 0.2867975
## 12 0.92 9 70 52 18 51 28456 0.4234693 0.5765307
## 13 0.58 9 70 52 18 51 28456 0.5390818 0.4609182
## 14 0.27 3 63 29 34 66 38380 0.3715617 0.6284383
## 15 0.60 9 70 52 18 51 28456 0.4143632 0.5856368
## 16 0.36 2 81 44 37 76 80841 0.4881116 0.5118884
## .pred_class
## 1 TOTS
## 2 Normal
## 3 Normal
## 4 TOTS
## 5 Normal
## 6 Normal
## 7 Normal
## 8 TOTS
## 9 Normal
## 10 Normal
## 11 Normal
## 12 TOTS
## 13 Normal
## 14 TOTS
## 15 TOTS
## 16 TOTS## Player revision position Int TklW OG PKcon Nation Squad
## 1 Wout Weghorst Normal ST 6 11 0 0 NED Wolfsburg
## 2 Robert Lewandowski Normal ST 6 12 0 0 POL Bayern Munich
## 3 Erling Haaland Normal ST 4 6 0 0 NOR Dortmund
## 4 Andre Silva Normal ST 4 4 0 0 POR Eint Frankfurt
## 5 Sasa Kalajdzic Normal ST 5 6 0 0 AUT Stuttgart
## Age Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY
## 1 28 1992 31 2671 29.7 20 7 18 2 3 3
## 2 32 1988 26 2188 24.3 36 6 30 6 7 4
## 3 20 2000 26 2227 24.7 25 5 23 2 4 2
## 4 25 1995 29 2490 27.7 25 6 19 6 6 1
## 5 23 1997 30 1874 20.8 14 4 14 0 0 1
## CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0 0.67 0.24 0.91 0.61 0.84
## 2 0 1.48 0.25 1.73 1.23 1.48
## 3 0 1.01 0.20 1.21 0.93 1.13
## 4 0 0.90 0.22 1.12 0.69 0.90
## 5 0 0.67 0.19 0.86 0.67 0.86
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 3 54 32 22 57 610 0.1836949 0.8163051 TOTS
## 2 Matches 1 86 40 46 71 NA 0.2182829 0.7817171 TOTS
## 3 Matches 5 66 42 24 55 1407 0.2547202 0.7452798 TOTS
## 4 Matches 4 62 47 15 56 967 0.2894835 0.7105165 TOTS
## 5 Matches 10 52 51 1 39 1108 0.5304433 0.4695567 Normal## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 Thomas Muller Normal CAM 17 32 0 0 GER Bayern Munich 31
## 2 Dani Olmo Normal CAM 23 22 0 0 ESP RB Leipzig 22
## 3 Leroy Sane Normal LM 11 19 0 0 GER Bayern Munich 25
## 4 Leon Bailey Normal LM 18 12 0 0 JAM Leverkusen 23
## 5 Joshua Kimmich Normal CDM 36 29 0 0 GER Bayern Munich 26
## Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 1989 29 2453 27.3 10 17 9 1 1 0 0
## 2 1998 31 2104 23.4 4 9 3 1 1 1 0
## 3 1996 29 1672 18.6 4 9 4 0 0 2 0
## 4 1997 29 2096 23.3 9 8 9 0 0 6 0
## 5 1995 24 1924 21.4 3 10 3 0 0 4 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.37 0.62 0.99 0.33 0.95
## 2 0.17 0.38 0.56 0.13 0.51
## 3 0.22 0.48 0.70 0.22 0.70
## 4 0.39 0.34 0.73 0.39 0.73
## 5 0.14 0.47 0.61 0.14 0.61
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 1 86 40 46 71 NA 0.2683768 0.7316232 TOTS
## 2 Matches 2 55 25 30 64 1125 0.3308795 0.6691205 TOTS
## 3 Matches 1 86 40 46 71 NA 0.3702325 0.6297675 TOTS
## 4 Matches 6 51 35 16 50 378 0.4128130 0.5871870 TOTS
## 5 Matches 1 86 40 46 71 NA 0.4143416 0.5856584 TOTS## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 David Alaba Normal CB 34 28 0 0 AUT Bayern Munich 28
## 2 Willi Orban Normal CB 29 22 0 0 HUN RB Leipzig 28
## 3 Ridle Baku Normal RB 43 26 0 0 GER Wolfsburg 23
## 4 Angelino Normal LB 29 16 0 0 ESP RB Leipzig 24
## 5 Jerome Boateng Normal CB 42 17 0 0 GER Bayern Munich 32
## Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 1992 29 2454 27.3 2 2 2 0 0 3 0
## 2 1992 26 2093 23.3 4 1 4 0 0 4 0
## 3 1998 29 2409 26.8 6 4 6 0 0 0 0
## 4 1997 24 2042 22.7 4 4 4 0 0 2 0
## 5 1988 26 2148 23.9 1 1 1 0 0 6 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.07 0.07 0.15 0.07 0.15
## 2 0.17 0.04 0.22 0.17 0.22
## 3 0.22 0.15 0.37 0.22 0.37
## 4 0.18 0.18 0.35 0.18 0.35
## 5 0.04 0.04 0.08 0.04 0.08
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 1 86 40 46 71 NA 0.3671121 0.6328879 TOTS
## 2 Matches 2 55 25 30 64 1125 0.4283482 0.5716518 TOTS
## 3 Matches 3 54 32 22 57 610 0.4316760 0.5683240 TOTS
## 4 Matches 2 55 25 30 64 1125 0.4762294 0.5237706 TOTS
## 5 Matches 1 86 40 46 71 NA 0.5025900 0.4974100 Normal
Bundesliga Team of the Season
Italian Serie A
## Warning: Removed 19578 rows containing non-finite values (stat_bin).
## Truth
## Prediction Normal TOTS
## Normal 115 8
## TOTS 4 9## Truth
## Prediction Normal TOTS
## Normal 116 8
## TOTS 3 9## Player revision position Int TklW OG PKcon Nation Squad
## 1 Mattia Caldara 17 TOTS CB 90 36 0 0 ITA Atalanta
## 2 Giorgio Chiellini 18 TOTS CB 28 15 0 0 ITA Juventus
## 3 Federico Chiesa 18 TOTS RM 9 37 0 0 ITA Fiorentina
## 4 Edin Dzeko 18 Normal ST 2 10 0 0 BIH Roma
## 5 Fabio Quagliarella 18 TOTS ST 8 9 0 0 ITA Sampdoria
## 6 Emre Can 19 TOTS CM 21 58 1 1 GER Juventus
## 7 Giorgio Chiellini 19 TOTS CB 23 9 0 0 ITA Juventus
## 8 Rodrigo De Paul 19 TOTS CM 36 31 0 1 ARG Udinese
## 9 Paulo Dybala 19 Normal CAM 15 12 0 0 ARG Juventus
## 10 Mario Mandzukic 19 Normal ST 12 22 0 0 CRO Juventus
## 11 Allan 19 TOTS CM 16 92 0 0 BRA Napoli
## Age Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY
## 1 22 1994 30 2655 29.5 7 0 7 0 0 4
## 2 32 1984 26 2161 24.0 0 1 0 0 0 2
## 3 19 1997 36 3012 33.5 6 4 6 0 0 7
## 4 31 1986 36 3018 33.5 16 3 16 0 0 6
## 5 34 1983 35 2719 30.2 19 5 12 7 8 4
## 6 24 1994 29 1811 20.1 4 1 3 1 1 7
## 7 33 1984 25 1991 22.1 1 1 1 0 0 3
## 8 24 1994 36 3189 35.4 9 9 6 3 6 7
## 9 24 1993 30 2137 23.7 5 4 4 1 1 2
## 10 32 1986 25 2014 22.4 9 6 9 0 0 4
## 11 27 1991 33 2616 29.1 1 3 1 0 0 10
## CrdR G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0 0.24 0.00 0.24 0.24 0.24
## 2 0 0.00 0.04 0.04 0.00 0.04
## 3 0 0.18 0.12 0.30 0.18 0.30
## 4 0 0.48 0.09 0.57 0.48 0.57
## 5 0 0.63 0.17 0.79 0.40 0.56
## 6 0 0.20 0.05 0.25 0.15 0.20
## 7 0 0.05 0.05 0.09 0.05 0.09
## 8 0 0.25 0.25 0.51 0.17 0.42
## 9 0 0.21 0.17 0.38 0.17 0.34
## 10 0 0.40 0.27 0.67 0.40 0.67
## 11 0 0.03 0.10 0.14 0.03 0.14
## Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 4 62 41 21 72 16948 0.5961494 0.4038506 Normal
## 2 1 86 24 62 95 39316 0.7483961 0.2516039 Normal
## 3 8 54 46 8 57 26092 0.6766323 0.3233677 Normal
## 4 3 61 28 33 77 37450 0.3786550 0.6213450 TOTS
## 5 10 56 60 -4 54 20156 0.6994372 0.3005628 Normal
## 6 1 70 30 40 90 37799 0.6401405 0.3598595 Normal
## 7 1 70 30 40 90 37799 0.7683505 0.2316495 Normal
## 8 12 39 53 -14 43 20414 0.7372084 0.2627916 Normal
## 9 1 70 30 40 90 37799 0.4620869 0.5379131 TOTS
## 10 1 70 30 40 90 37799 0.3968211 0.6031789 TOTS
## 11 2 74 36 38 79 29003 0.7035211 0.2964789 Normal## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 Duvan Zapata Normal ST 9 6 0 0 COL Atalanta 30
## 2 Romelu Lukaku Normal ST 2 3 0 0 BEL Inter 27
## 3 Cristiano Ronaldo Normal ST 5 2 0 0 POR Juventus 36
## 4 Alvaro Morata Normal ST 9 11 0 0 ESP Juventus 28
## 5 Dries Mertens Normal CF 8 15 0 0 BEL Napoli 33
## Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 1991 32 2052 22.8 14 7 13 1 1 0 0
## 2 1993 32 2580 28.7 21 9 16 5 5 4 0
## 3 1985 29 2463 27.4 25 2 20 5 6 3 0
## 4 1992 28 1788 19.9 9 9 8 1 1 2 1
## 5 1987 24 1382 15.4 9 8 9 0 0 2 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.61 0.31 0.92 0.57 0.88
## 2 0.73 0.31 1.05 0.56 0.87
## 3 0.91 0.07 0.99 0.73 0.80
## 4 0.45 0.45 0.91 0.40 0.86
## 5 0.59 0.52 1.11 0.59 1.11
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 2 78 39 39 68 118 0.1950023 0.8049977 TOTS
## 2 Matches 1 72 29 43 79 125 0.2367392 0.7632608 TOTS
## 3 Matches 3 65 30 35 66 118 0.2532262 0.7467738 TOTS
## 4 Matches 3 65 30 35 66 118 0.2801252 0.7198748 TOTS
## 5 Matches 4 73 37 36 66 125 0.2943554 0.7056446 TOTS## Player revision position Int TklW OG PKcon Nation Squad Age
## 1 Piotr Zielinski Normal CM 16 14 0 0 POL Napoli 26
## 2 Lorenzo Insigne Normal LM 34 11 0 0 ITA Napoli 29
## 3 Matteo Politano Normal RM 19 11 0 0 ITA Napoli 27
## 4 Hirving Lozano Normal RM 13 18 0 0 MEX Napoli 25
## 5 Ruslan Malinovskyi Normal CM 3 13 0 0 UKR Atalanta 27
## Born MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 1994 31 2154 23.9 6 8 6 0 0 2 0
## 2 1991 30 2415 26.8 17 6 10 7 7 2 1
## 3 1993 32 1696 18.8 9 4 9 0 0 3 0
## 4 1995 27 1727 19.2 9 3 9 0 0 5 0
## 5 1993 31 1525 16.9 6 9 5 1 1 3 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.25 0.33 0.58 0.25 0.58
## 2 0.63 0.22 0.86 0.37 0.60
## 3 0.48 0.21 0.69 0.48 0.69
## 4 0.47 0.16 0.63 0.47 0.63
## 5 0.35 0.53 0.89 0.30 0.83
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 4 73 37 36 66 125 0.3213239 0.6786761 TOTS
## 2 Matches 4 73 37 36 66 125 0.3879365 0.6120635 TOTS
## 3 Matches 4 73 37 36 66 125 0.3932205 0.6067795 TOTS
## 4 Matches 4 73 37 36 66 125 0.4057160 0.5942840 TOTS
## 5 Matches 2 78 39 39 68 118 0.4160895 0.5839105 TOTS## Player revision position Int TklW OG PKcon Nation Squad Age Born
## 1 Juan Cuadrado Normal RB 20 21 0 0 COL Juventus 32 1988
## 2 Milan Skriniar Normal CB 26 26 0 0 SVK Inter 26 1995
## 3 Cristian Romero Normal CB 70 42 0 0 ARG Atalanta 23 1998
## 4 Rafael Toloi Normal CB 35 28 0 1 ITA Atalanta 30 1990
## 5 Stefan de Vrij Normal CB 39 15 0 0 NED Inter 29 1992
## MP Min minutes_played_divided_by90 Gls Ast Non_PK_G PK PKatt CrdY CrdR
## 1 25 1812 20.1 0 10 0 0 0 8 1
## 2 29 2507 27.9 3 0 3 0 0 1 0
## 3 26 2095 23.3 2 2 2 0 0 10 0
## 4 28 2283 25.4 2 0 2 0 0 7 0
## 5 30 2554 28.4 1 0 1 0 0 2 0
## G_per90 A_per90 G_plus_A_per90 G_minus_Pk_per90 G_plus_A_minus_PK_per90
## 1 0.00 0.50 0.50 0.00 0.50
## 2 0.11 0.00 0.11 0.11 0.11
## 3 0.09 0.09 0.17 0.09 0.17
## 4 0.08 0.00 0.08 0.08 0.08
## 5 0.04 0.00 0.04 0.04 0.04
## Matches Rk GF GA GD Pts Attendance .pred_Normal .pred_TOTS .pred_class
## 1 Matches 3 65 30 35 66 118 0.6088147 0.3911853 Normal
## 2 Matches 1 72 29 43 79 125 0.6101755 0.3898245 Normal
## 3 Matches 2 78 39 39 68 118 0.6616336 0.3383664 Normal
## 4 Matches 2 78 39 39 68 118 0.6779792 0.3220208 Normal
## 5 Matches 1 72 29 43 79 125 0.7033063 0.2966937 Normal
Serie A Team of the Season